Titlebook: Data Mining in Large Sets of Complex Data; Robson L. F. Cordeiro,Christos Faloutsos,Caetano T Book 2013 The Author(s) 2013 Analysis of Bre

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Clustering Methods for Moderate-to-High Dimensionality Data,ter, we discuss the main reasons that lead to this fact. It is also mentioned that the use of dimensionality reduction methods does not solve the problem, since it allows one to treat only the global correlations in the data. Correlations local to subsets of the data cannot be identified without the

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QMAS,mining tasks-the tasks of labeling and summarizing large sets of complex data. Given a large collection of complex objects, . of which have labels, how can we guess the labels of the remaining majority, and how can we spot those objects that may need brand new labels, different from the existing one

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Glandular Fever (Infectious Mononucleosis)tasets that must be submitted for data mining processes. However, given a . dataset of moderate-to-high dimensionality, how could one cluster its points? Numerous successful, serial clustering algorithms for data in five or more dimensions exist in literature, including the algorithm . that we descr